Exploration of applying a theory-based user classification model to inform personalised content-based image retrieval system design

© ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published at http://dl.acm.org/citation.cfm?id=2903636To better understand users and create more personalised search experiences, a number of user models have been developed, usually based on different theories or empirical data study. After developing the user models, it is important to effectively utilise them in the design, development and evaluation of search systems to improve users’ overall search experiences. However there is a lack of research has been done on the utilisation of the user models especially theory-based models, because of the challenges on the utilization methodologies when applying the model to different search systems. This paper explores and states how to apply an Information Foraging Theory (IFT) based user classification model called ISE to effectively identify user’s search characteristics and create user groups, based on an empirically-driven methodology for content-based image retrieval (CBIR) systems and how the preferences of different user types inform the personalized design of the CBIR systems

Evaluating Web Search Engines Results for Personalization and User Tracking

Recently, light has been shed on the trend of personalization, which comes into play whenever different search results are being tailored for a group of users who have issued the same search query. The unpalatable fact that myriads of search results are being manipulated has perturbed a horde of people. With regards to that, personalization can be instrumental in spurring the Filter Bubble effects, which revolves around the inability of certain users to gain access to the typified contents that are allegedly irrelevant per the search engine's algorithm. In harmony with that, there is a wealth of research on this area. Each of these has relied on using techniques revolving around creating Google accounts that differ in one feature and issuing identical search queries from each account. The search results are often compared to determine whether those results are going to vary per account. Thereupon, we have conducted six experiments that aim to closely inspect and spot the patterns of personalization in search results. In a like manner, we are going to examine how the search results are going to vary accordingly. In all of the tasks, three different metrics are going to be measured, namely, the number of total hits, the first hit, and the correlation between hits. Those experiments are centered around fulfilling the following tasks. Firstly, setting up four VPNs that are located at different geographic locations and comparing the search results with those obtained in the UAE. Secondly, performing the search while logging in and out of a Google account. Thirdly, searching while connecting to different networks: home, phone, and university networks. Fourthly, using different search engines to issue the search queries. Fifthly, using different web browsers to carry out the search process. Finally, creating and training six Google accounts

Testing the stability of “wisdom of crowds” judgments of search results over time and their similarity with the search engine rankings

PURPOSE: One of the under-explored aspects in the process of user information seeking behaviour is influence of time on relevance evaluation. It has been shown in previous studies that individual users might change their assessment of search results over time. It is also known that aggregated judgments of multiple individual users can lead to correct and reliable decisions; this phenomenon is known as the “wisdom of crowds”. The aim of this study is to examine whether aggregated judgments will be more stable and thus more reliable over time than individual user judgments. DESIGN/METHODS: In this study two simple measures are proposed to calculate the aggregated judgments of search results and compare their reliability and stability to individual user judgments. In addition, the aggregated “wisdom of crowds” judgments were used as a means to compare the differences between human assessments of search results and search engine’s rankings. A large-scale user study was conducted with 87 participants who evaluated two different queries and four diverse result sets twice, with an interval of two months. Two types of judgments were considered in this study: 1) relevance on a 4-point scale, and 2) ranking on a 10-point scale without ties. FINDINGS: It was found that aggregated judgments are much more stable than individual user judgments, yet they are quite different from search engine rankings. Practical implications: The proposed “wisdom of crowds” based approach provides a reliable reference point for the evaluation of search engines. This is also important for exploring the need of personalization and adapting search engine’s ranking over time to changes in users preferences. ORIGINALITY/VALUE: This is a first study that applies the notion of “wisdom of crowds” to examine the under-explored phenomenon in the literature of “change in time” in user evaluation of relevance

Towards a Framework of Personalization Techniques

This paper aims to elaborate on the role of user modelling for personalization and enhanced attention support. User modelling is an important element in the management of personal profiles and identity of users, but also a key element for providing adaptive features and personalized interaction. In this paper, we present personalization as the process consisting on the customization, and the adaptation of the interaction along the structure, the content, the modality, the presentation and the level of attention required. The paper surveys personalization techniques and provides concrete examples of personalized interaction. In particular, the paper focuses on the role of user modeling for enhanced, personalized user support within interactive applications. The key contribution of the paper is to propose a framework of personalization techniques and to identify new forms of personalization that aim at taking into account human cognitive capabilities and emotions

'Think of it first as an advertising system': personalisierte Online-Suche als Datenlieferant des Marketings

'Suchmaschinen gehören seit langem zu den wichtigsten Werbeträgern im Netz und es wird mittlerweile offen zugestanden, dass die gezielte Vermarktung von Werbeplätzen sich zur Kernaufgabe der Suchmaschinenbetreiber entwickelt hat. Um dem Ruf nach relevanteren Suchergebnissen nachkommen zu können, binden neue Formen der personalisierten Suche immer weitere Bereiche des Nutzerverhaltens in den Suchprozess ein, gleichzeitig schaffen die gesammelten Daten aber auch die Grundlage für eine noch engere Verzahnung ökonomischer Interessen mit dem persönlichen Nutzungskontext. Mit Bezug auf aktuelle Theoriebildung aus den 'Surveillance studies' diskutiert der Beitrag die Rolle der personalisierten Suche als Bindeglied zwischen Nutzer und Werbung. Sowohl die Entwicklung der Online-Werbung als auch die technischen Grundlagen der personalisierten Suche werden skizziert, um schließlich an zwei konkreten Beispielen zu erläutern, welche Daten bei der personalisierten Suche erhoben werden und wie diese zu Werbezwecken verwendet werden können. Dabei wird deutlich, dass die zunächst zur Verbesserung der Suchergebnisse erhobenen Nutzerinformationen einem immer stärkeren kommerziellen Verwertungsdruck ausgesetzt sind.' (Autorenreferat

Web information search and sharing :

制度:新 ; 報告番号:甲2735号 ; 学位の種類:博士(人間科学) ; 授与年月日:2009/3/15 ; 早大学位記番号:新493